Diagnosis of Diabetes from Tongue Image Using Versatile Tooth-Marked Region Classification

Abstract

Image analysis of the human tongue has been found to be useful in detecting various diseases in the body. The tongue image indicates the condition of different parts of the body and the changes in the tongue reflect the misbehavior of the internal parts of the body. So, the diagnosis of diseases is very much needed.The human tongue is detected and extracted effectively by implementing adaptive threshold segmentation. Gabor filter is used to identify the color and texture of the image. From the factors such as color, texture (coating), smoothness/cracks and size, the healthiness of the tongue is analyzed effectively. The threshold value taken from a healthy human tongue is used to classify a person’s tongue whether it is normal or abnormal. If the tongue is abnormal, diseases, such as, thyroid, ulcer and diabetes can be diagnosed.  In this paper, diagnosis of diabetes from tongue image using Versatile Tooth-Marked Region (VTMR) classification method is proposed. The proposed VTMR method is tested on the 96 BioHit tongue images collected from Sri Muthukumaran Medical College Hospital and Research Institute and 97 UV scanned tongue images captured from the patients by using IPhone with HD camera. The simulation work is carried out in MATLAB simulation environment utilizing the proposed VTMR method. The proposed VTMR method uses color and texture features for the diagnosis of diabetes. The color and texture features are beneficial to classify the tongue image and to diagnose diseases.